A life circle planning evaluation method, device and equipment based on space supply-demand matching

Abstract

The application provides a life circle planning evaluation method and device based on space supply-demand matching, and belongs to the technical field of urban planning technology, and specifically comprises the following steps: obtaining multi-dimensional geographic data of a target region, determining an isochrone service area of an existing device, obtaining service coverage of the device, determining a service blind area that is not covered by the service, performing spatial discrete grid processing on the target region to generate an evaluation grid set, determining initial site selection of facilities that can be reached within a predetermined time range based on the service blind area and the evaluation grid set, performing weighted calculation on service population of the initial site selection of the facilities, obtaining a site selection efficiency index, screening and optimizing a theoretical planning land block and the initial site selection of the facilities based on the site selection efficiency index, and generating a relatively optimal site selection scheme combination. Through the processing scheme, global traversal search is realized, global optimal solution is ensured, a quantitative evaluation system is established, scientific decision support is provided, blind areas are accurately focused, and efficient supply-demand matching is realized.

Description

Technical Field

[0001] This invention belongs to the field of urban planning technology, and in particular to a method, apparatus and equipment for evaluating living circle planning based on spatial supply and demand matching. Background Technology

[0002] With the continuous improvement of urban governance sophistication, the scientific site selection of public service facilities has become a crucial aspect of enhancing residents' quality of life. However, current urban planning support technologies still have significant limitations in addressing the complex needs of "community living circles," failing to meet the comprehensive requirements of government decision-making for scientific rigor, systematic approach, and multi-scheme comparison. Existing technologies mainly face the following core challenges: 1. Existing technologies lack a closed-loop logic of "assessment-diagnosis-governance": Traditional planning assistance methods are often fragmented. Existing technologies are mostly limited to single-dimensional analysis, such as only performing static service radius coverage calculations, or simply comparing a few pre-set candidate points. This fragmented analysis model results in a lack of rigorous logical derivation between the assessment results and the final site selection plan. Planners often need to complete "current status assessment" and "plan generation" separately in different software or processes, resulting in a lack of direct data-level correlation between "where services are lacking" and "where to supplement them," failing to form an automated closed loop from problem identification to plan generation.

[0003] 2. Lack of a "multi-option generation and quantitative comparison" mechanism for government decision-making: In actual government administrative approval and decision-making processes, multiple alternative options are often required for weighing (such as the trade-off between "prioritizing population coverage" and "prioritizing the use of existing space"). However, existing auxiliary technologies typically only provide a single, experience-based "recommended location," or rely on manual "scattering" of points on a map for trial and error. This model lacks the ability to traverse the entire potential space and cannot generate multiple strategy-oriented candidate solutions based on the same set of data standards in a short period of time, resulting in insufficient decision support and difficulty in coping with complex multi-party game requirements.

[0004] 3. Traditional methods struggle to achieve global optimization through precise matching of supply and demand: Existing site selection tools often rely on straight-line distances (Euclidean distance) or simplified road network models during calculations, neglecting real-world urban pedestrian obstacles and path travel time. More importantly, traditional methods typically fail to deeply couple the calculations of "population demand weights" with "spatial supply potential." Lacking a global perspective, site selection dominated by human experience is prone to falling into local optima traps, resulting in solutions that, while seemingly reasonable locally, fail to maximize social benefits from a macro-level perspective of the entire street or community.

[0005] In summary, existing technologies lack a method that can seamlessly integrate multi-source spatial data fusion, real road network analysis, blind spot diagnosis, and automated site selection throughout the entire process. There is an urgent need for a systematic technical solution that can assist government departments in generating multiple strategy-oriented site selection schemes from a macro-level perspective and making scientific decisions based on quantitative indicators. Summary of the Invention

[0006] Therefore, to overcome the shortcomings of the prior art, this invention provides a method, apparatus, and equipment capable of integrating multi-source spatial data fusion, real road network analysis, blind spot diagnosis, and automated site selection throughout the entire process. This systematic technical solution can assist government departments in generating various strategy-oriented site selection schemes from a macro-level perspective and making scientific decisions based on quantitative indicators.

[0007] To achieve the above objectives, the present invention provides a method for evaluating living circle planning based on spatial supply and demand matching, comprising: Acquire multidimensional geographic data of the target area, and extract discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data; based on the road network map and facility locations in the target area, determine the residential communities accessible to the facilities within a predetermined time range, and generate isochronous service areas accordingly; based on the isochronous service areas, obtain the service coverage rate of the facility locations in the target area; overlay the isochronous service areas with the residential community map in the target area to determine the residential areas in the target area that are not covered by services, and construct a dataset of service blind spots; perform spatial discrete gridding processing on the target area to generate an evaluation grid set; based on the service blind spots and the evaluation grid set, determine the initial site selection of facilities accessible within a predetermined time range; use the population weight of the target area to perform a weighted summation of the service coverage range of the initial site selection of facilities, and calculate the site selection efficiency index of the initial site selection of facilities; based on the site selection efficiency index, filter the provided theoretically plannable land parcels and the initial site selection of facilities to generate a better combination of site selection schemes.

[0008] In one embodiment, acquiring multidimensional geographic data of the target area and extracting road network maps from the multidimensional geographic data, including discretely distributed facility locations and road network maps from each residential community to each facility location, includes: delineating the target area; acquiring multidimensional geographic data within the target area, including public service facility data, pedestrian road network topology data, land use classification data, and population statistics spatial data; parsing the multidimensional geographic data and uniformly converting all multidimensional geographic data to a selected spatial reference coordinate system; identifying and extracting discretely distributed facility locations from the multidimensional geographic data; and generating road network maps from each residential community to each facility location based on the road network.

[0009] In one embodiment, the step of determining the residential communities reachable from the facilities within a predetermined time range based on the road network map and the facility locations in the target area, and generating isochronous service areas accordingly, and obtaining the service coverage rate of the facility locations in the target area based on the isochronous service areas, includes: constructing a topological network graph containing nodes and edges based on the road network map in the target area, using road segment travel time as edge weights, establishing a time cost resistance surface based on the topological network graph and the weights of each edge, and quantifying the travel time of different road segments; using the facility points as the source points, executing a graph traversal algorithm to calculate the cumulative minimum time cost from the source point to all nodes in the network, generating a shortest path tree, and extracting a set of boundary points for areas where the time cost is not greater than a time threshold; generating a closed isochronous service area based on the boundary point set using a geometric closure algorithm; and calculating the service coverage rate of the facilities in the target area, wherein the service coverage rate includes spatial coverage rate and population coverage rate.

[0010] In one embodiment, the isochronous cycle is calculated as follows: Set time threshold Filter out cumulative time costs ≤ The reachable subgraph; linear interpolation is performed on edges that cross the threshold to accurately locate isochronous boundary points; During the shortest path tree traversal, if there exists an edge Connect to reachable nodes and unreachable nodes And satisfy: This edge is then a critical edge, and the system calculates the isochronous boundary points on this edge using linear interpolation. Positional proportions :

[0011] Boundary points The coordinates (x, y) are calculated as follows:

[0012] in From source to node The cumulative minimum time, The set service time threshold (e.g., 15 minutes). For the edge The passage time, , They are nodes and Spatial coordinates.

[0013] In one embodiment, the step of overlaying the isochronous service area with a residential community map in the target area to determine the residential areas in the target area that are not covered by service, and constructing a dataset of service blind spots, includes: The service area of ​​the isochronous circle is overlaid with the residential community map in the target area, and the geometric intersection of each residential community and the service area is calculated. If the area of ​​a residential community falling into the service area is less than a set value, it is identified as a non-compliant community, and all non-compliant communities are extracted as a service blind spot dataset.

[0014] In one embodiment, the process of spatially discretizing and gridding the target area to generate an evaluation grid set, determining the initial facility location reachable within a predetermined time range based on the service blind spot and the evaluation grid set, weighting the service coverage of the initial facility location using the population weight of the target area, and calculating the location efficiency index of the initial facility location includes: The target area is spatially discretized into a grid to generate an evaluation grid set G covering the entire target area. Based on the service blind spot and the evaluation grid set, the coverage capability and accessibility of each evaluation grid to the service blind spot within a predetermined time range are calculated. Candidate grids that can effectively cover the service blind spot within the predetermined time range are selected as the initial site selection for the facility. Based on the population weight of the target area, the service coverage of the initial site selection for the facility is calculated by community population weighted summation to obtain the corresponding site selection efficiency index.

[0015] In one embodiment, the identification of the theoretically planarable land parcels includes: Spatial overlay analysis was used to analyze existing and planned land parcel data to identify potential land parcels suitable for new facility construction. Spatial intersection analysis was then performed between the initial facility sites and these potential land parcels to extract all initial facility sites that spatially intersect with the potential land parcels, thus constructing a potential site set. According to the site selection efficiency index, for All grids are sorted in descending order to obtain an ordered candidate list. Based on the user's specified facility quantity requirements, generate a combination of optimal site selection schemes.

[0016] In one embodiment, it includes: Using existing land parcel data as the baseline layer and planned land parcel data as the overlay layer, the baseline layer is topologically cut based on the boundary geometric information of the overlay layer to generate cut patches; a spatial location index is established, and the planned land use type attributes of the overlay layer are mapped to the cut patches through spatial connections; the difference between the existing land use type and the planned land use type of the cut patches is judged, and patches with inconsistent land use type categories are extracted as potential construction sites.

[0017] A living circle planning assessment device based on spatial supply and demand matching, the device comprising: The data acquisition module is used to acquire multidimensional geographic data of the target area and extract discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data. The facility service efficiency assessment module is used to determine the residential communities reachable within a predetermined time range of the facility based on the road network map and the facility locations in the target area, generate isochronous service areas accordingly, and calculate the service coverage rate of the facility locations in the target area based on the isochronous service areas using network analysis. The service blind spot identification module is used to spatially overlay the isochronous service area with the existing map of the target area to locate the uncovered area within the target area and construct a service blind spot dataset. The site selection efficiency quantification module performs spatial discrete grid processing on the target area to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, it determines the initial site selection of facilities that can be reached within a predetermined time range. It uses the population weight of the target area to weight the service coverage of the initial site selection of facilities and calculates the site selection efficiency index of the initial site selection of facilities. The site selection scheme generation module identifies potential plots from the provided existing and planned plots, and generates a better combination of site selection schemes based on the potential plots and the site selection efficiency index.

[0018] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described method.

[0019] Compared with the prior art, the advantages of the present invention are as follows: 1. By precisely targeting blind spots and achieving efficient matching of supply and demand, this application innovatively introduces a "service blind spot set" as a calculation constraint, ensuring that the site selection efficiency index is determined solely by the population contribution of the uncovered areas. This guides new facilities to automatically cluster in areas with the "most lacking facilities and the highest population density," realizing a paradigm shift from "rough selection based on experience" to "data-driven precise blind spot filling," maximizing the social benefits of public financial investment.

[0020] 2. To achieve a global traversal search and ensure a globally optimal solution, this application constructs a high-resolution evaluation grid and a site selection efficiency index, discretizing the continuous space into tens of thousands of computational units and performing automated efficiency calculations on each unit. This overcomes the limitation of manual comparison of only a "few points," achieving a full-space, blind-spot-free scan and evaluation of the target area. The system can automatically uncover "high-efficiency hotspots" that are difficult for human experience to detect, ensuring that the site selection results are mathematically optimal and effectively avoiding the omission of high-quality sites.

[0021] 3. Establishing a quantitative evaluation system to provide scientific decision-making support: This application proposes a quantitative calculation based on "population weight - distance decay," transforming the abstract concept of "site selection merit" into concrete numerical indicators. The service efficiency of each potential site can be accurately calculated. Different schemes and different plots can be directly ranked and selected based on numerical values, eliminating the subjective arbitrariness of human scoring. The output quantitative results even take into account subsequent urban planning and provide solid data evidence to ensure the accuracy of site selection, significantly improving the scientific nature and credibility of the decision-making process. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the living circle planning and evaluation method based on spatial supply and demand matching in an embodiment of the present invention. Detailed Implementation

[0024] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0025] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that the following description covers various aspects of embodiments within the scope of protection of this invention. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0027] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0028] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the aspects described can be practiced without these specific details.

[0029] Existing methods for selecting public service facilities lack a closed-loop logic of "assessment-diagnosis-governance": traditional planning support methods are often fragmented. Existing technologies are mostly limited to single-dimensional analysis, such as performing only static service radius coverage calculations or simple comparisons of a few pre-set candidate sites. This fragmented analysis model results in a lack of rigorous logical derivation between the assessment results and the final site selection plan. Planners often need to complete "current situation assessment" and "plan generation" separately in different software or processes, leading to a lack of direct data-level correlation between "where services are lacking" and "where to supplement them," failing to form an automated closed loop from problem identification to plan generation.

[0030] The lack of a "multi-option generation and quantitative comparison" mechanism for government decision-making: In actual government administrative approval and decision-making processes, multiple alternative options are often required for weighing (such as the trade-off between "prioritizing population coverage" and "prioritizing the use of existing space"). However, existing auxiliary technologies typically only provide a single, experience-based "recommended location," or rely on manual "scattering" of points on a map for trial and error. This model lacks the ability to traverse the entire potential space and cannot generate multiple strategy-oriented candidate solutions based on the same set of data standards in a short period of time, resulting in insufficient decision support and difficulty in coping with complex multi-party game requirements.

[0031] Traditional methods struggle to achieve global optimization through precise matching of supply and demand: existing site selection tools often rely on straight-line distances (Euclidean distance) or simplified road network models during calculations, neglecting real-world urban pedestrian obstacles and path travel times. More importantly, traditional methods typically fail to deeply couple the calculations of "population demand weights" with "spatial supply potential." Lacking a global perspective, site selection dominated by human experience is highly susceptible to falling into local optima traps, resulting in solutions that, while seemingly reasonable locally, fail to maximize social benefits from a macro-level perspective of the entire street or community.

[0032] In view of this, the present invention proposes a living circle planning evaluation method based on spatial supply and demand matching, which can realize full-domain traversal search, ensure the global optimal solution, establish a quantitative evaluation system, provide scientific decision support, accurately focus on blind spots, and achieve efficient supply and demand matching.

[0033] This living circle planning evaluation method based on spatial supply and demand matching can be applied to servers or terminals. Terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets and portable smart devices. Servers can be implemented using independent servers or server clusters composed of multiple servers.

[0034] like Figure 1 As shown, this application provides a method for evaluating living circle planning based on spatial supply and demand matching. Taking the application of this method to a server as an example, it includes the following steps: Step 101: Obtain multidimensional geographic data of the target area, and extract from the multidimensional geographic data a road network map containing discretely distributed facility locations and the road network from each residential community to each facility location.

[0035] The server obtains multidimensional geographic data from the local government service data management bureau, including officially released data on public service facilities, pedestrian network topology, land use classification, and population statistics spatial data for the target area. The public service facility data includes the geometric coordinates of public service facilities and their attribute fields, with the attribute fields at least covering the facility type. The pedestrian network topology data includes the geometric line elements of road centerlines and pedestrian paths, with these line elements carrying node topological relationships and travel time attributes, including at least road segment length, design speed, or road grade. The land use classification data includes existing land use maps and planned land use maps, with the maps containing land use codes and boundary geometric information. The population statistics spatial data includes spatial area maps of residential communities and their associated population statistics attribute data, with the population data including at least the number of permanent residents and their age structure.

[0036] The server performs comprehensive analysis of multidimensional geographic data, clarifying the geometric information, attribute fields, and data formats of various data types, and outlining the data association logic. All analyzed multidimensional geographic data is then uniformly converted to a selected spatial reference coordinate system to eliminate spatial misalignment issues caused by coordinate system differences, ensuring the accuracy of spatial associations among various data types.

[0037] Based on public service facility data after coordinate system one, the server identifies and filters discretely distributed facility locations, removes dependent or unlocatable locations, extracts the geometric coordinates and core attribute information of each location within a selected spatial reference coordinate system, and completes classification and archiving. Using the pedestrian road network topology data after coordinate system one, combined with the residential community spatial area map and the extracted discrete facility locations, a road network map is generated showing the routes from each residential community to the corresponding facility location within the selected spatial reference coordinate system, clarifying the road network topology and traffic-related attributes. The selected spatial reference coordinate system can be one of WGS1984, CGCS2000, BJ54, Xi'an 80, GCJ-02, BD-09, UTM, or Web Mercator.

[0038] Step 102: Based on the road network map and facility locations in the target area, determine the residential communities accessible within a predetermined time range of the facilities, and generate isochronous service areas accordingly. Based on the isochronous service areas, obtain the service coverage rate of the facility locations in the target area.

[0039] The server constructs a topological network graph containing nodes and edges based on the road network graph in the target area, uses the road segment travel time as the edge weight, and establishes a time cost resistance surface based on the topological network graph and the weights of each edge to quantify the travel time of different road segments.

[0040] For each edge in the road network (Represents a section of road), its travel time The calculation formula is:

[0041] in: This is the geometric length (meters) of the road segment. The design speed (meters per second) for this road section is preset with different values ​​based on the road class (such as expressway, secondary road, alley). Road condition correction factor (0 < ≤1), used to reflect the impact of slope, road conditions, or real-time congestion on speed reduction (if based solely on static data, it can be set to ≤1). =1).

[0042] The server uses the facility point as the source point, executes a graph traversal algorithm (such as Dijkstra's algorithm or A* algorithm), calculates the cumulative minimum time cost from the source point to all nodes in the network, and generates the shortest path tree.

[0043] The server extracts boundary points and sets time thresholds. (e.g., 15 minutes), filter for cumulative time costs ≤ The reachable subgraph; linear interpolation is performed on edges that cross the threshold to accurately locate isochronous boundary points.

[0044] During the shortest path tree traversal, if there exists an edge Connecting nodes (Reachable) and nodes (Unreachable), and satisfies: This edge is then a critical edge. The system calculates the isochronous boundary points on this edge using linear interpolation. Positional proportions :

[0045] Boundary points The coordinates (x, y) are calculated as follows:

[0046] in: From source to node The cumulative minimum time; The set service time threshold (e.g., 15 minutes); For the edge Passage time; , They are nodes and Spatial coordinates.

[0047] Based on the extracted boundary point set, the server uses a geometric closure algorithm (such as Alpha Shapes or the concave hull algorithm) to generate closed polygonal vector patterns, which are the isochronous service areas.

[0048] The server calculates the service coverage rate of this type of facility in the target area: the ratio of the area covered by the isochronous service area to the total area of ​​the target area is the spatial coverage rate; after the isochronous service area is superimposed with the population statistics spatial data, the total population falling within the service area is counted, and the service population of the existing facilities and the total population coverage rate are calculated.

[0049] Step 103: Overlay the isochronous service area with the residential community map in the target area to determine the residential areas in the target area that are not covered by services, and construct a dataset of service blind spots.

[0050] The server overlays the isochronous service area map with the residential community map in the target area, calculates the geometric intersection of each residential community in the community map with the service area, and identifies non-compliant communities. The method for identifying non-compliant communities can be either that the community's area falling within the service area is less than 30% or that the community is completely outside the service area. All non-compliant communities are then extracted as a service blind spot dataset. .

[0051] Step 104: Spatial discretization and gridding of the target area is performed to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, the initial site selection of facilities that can be reached within a predetermined time range is determined. The service coverage of the initial site selection of facilities is weighted by the population weight of the target area, and the site selection efficiency index of the initial site selection of facilities is calculated.

[0052] Set the grid size, typically 10m x 10m at a street scale, but this can be adjusted based on the estimated size of the site facilities. The server generates an evaluation grid set G = {...} covering the entire target area using the set grid. , ,..., Each grid cell is considered a potential candidate location. This enables a full-space, no-blind-spot traversal search of the target area, ensuring that no hidden but highly efficient optimal location is overlooked, thus guaranteeing the global optimality of the solution.

[0053] Each grid in the evaluation grid set is processed individually, and the server calculates the service blind zone set for each grid within a predetermined time range. Coverage and accessibility. Coverage is the coverage of the grid location. The spatial coverage and service range of service blind spots are assessed; accessibility refers to the ease of travel from the grid to the service blind spot, or from the blind spot to the grid, within a predetermined time period. Evaluation grids that simultaneously meet the coverage and accessibility requirements and can effectively cover the service blind spots within the predetermined time range are selected and designated as candidate grids for effective service blind spots. This serves as the initial site selection for facilities to be constructed, providing a foundation for subsequent site selection optimization and the final determination of optimal locations.

[0054] For each facility's initial site selection, define its effective coverage blind spot community set. :

[0055] For each grid where the initial site selection for a facility is located, the server obtains population data for the communities in the blind spots covered by that grid, and thus obtains the planned service population for the communities in the blind spots. The number of people served varies depending on the type of facility. For example, for health service facilities, the number of permanent residents in the community is used. For elderly care facilities, the number of people aged 60 and above in the community is used.

[0056] For each initial site selection, the server accumulates the population to be served by all the intersecting blind zone isochronous circles, obtaining the site selection efficiency index of that grid, denoted as . .

[0057]

[0058] Where: Vij represents the population contribution value, which is the sum of the planned service population brought about by all the blind zone isochrones intersecting with the initial site selection of the facility.

[0059] The value of Ei represents "how many blind-spot communities can be covered at once if a new facility is built at grid gi, and how many people will be served." The larger the Ei value, the higher the site selection efficiency, the more blind-spot population it can serve, and the higher its priority.

[0060] The server calculates the initial location of all facilities within the selected grid. Values ​​are used to generate a heatmap of site selection efficiency distribution. This includes the initial site selection for each facility across the entire region. The raster dataset of values ​​is directly used to guide the subsequent screening and sorting of potential land parcels. For The initial site selection for facilities with higher value is in the overlapping and intersection areas of multiple blind spots. Site selection in these locations can maximize the benefits of "building one point to solve a blind spot".

[0061] Step 105: Based on the site selection efficiency index, the provided theoretically planarable land parcels and the initial site selection of the facilities are screened to generate a better combination of site selection schemes.

[0062] The server performs spatial overlay analysis on the provided theoretically plannable land parcels and initial facility site selection parcels, accurately identifying the overlapping areas between the initial site selection grids and plannable land parcels, and clarifying the area proportion of each initial site selection grid within the plannable land parcel. Site selection locations that do not meet construction conditions or conflict with planning are eliminated, retaining potential grids that simultaneously meet both initial site selection requirements and buildable conditions. .

[0063] The server obtains the addressing requirements from the input, based on the reserved potential grid. The optimal combination of site selection schemes is selected based on the site selection efficiency index and the requirements.

[0064] The server outputs the final optimal solution, selecting the one with the best overall benefits from multiple candidate combinations as the final recommended facility layout scheme.

[0065] The above method, based on the spatial supply and demand matching living circle planning evaluation method, achieves the following effects in this invention. 1. By precisely targeting blind spots and achieving efficient matching of supply and demand, this application innovatively introduces a "service blind spot set" as a calculation constraint, ensuring that the site selection efficiency index is determined solely by the population contribution of the uncovered areas. This guides new facilities to automatically cluster in areas with the "most lacking facilities and the highest population density," realizing a paradigm shift from "rough selection based on experience" to "data-driven precise blind spot filling," maximizing the social benefits of public financial investment.

[0066] 2. To achieve a global traversal search and ensure a globally optimal solution, this application constructs a high-resolution evaluation grid and a site selection efficiency index, discretizing the continuous space into tens of thousands of computational units and performing automated efficiency calculations on each unit. This overcomes the limitation of manual comparison of only a "few points," achieving a full-space, blind-spot-free scan and evaluation of the target area. The system can automatically uncover "high-efficiency hotspots" that are difficult for human experience to detect, ensuring that the site selection results are mathematically optimal and effectively avoiding the omission of high-quality sites.

[0067] 3. Establishing a quantitative evaluation system to provide scientific decision-making support: This application proposes a quantitative calculation based on "population weight - distance decay," transforming the abstract concept of "site selection merit" into concrete numerical indicators. The service efficiency of each potential site can be accurately calculated. Different schemes and different plots can be directly ranked and selected based on numerical values, eliminating the subjective arbitrariness of human scoring. The output quantitative results even take into account subsequent urban planning and provide solid data evidence to ensure the accuracy of site selection, significantly improving the scientific nature and credibility of the decision-making process.

[0068] In one embodiment, acquiring multidimensional geographic data of the target area and extracting a road network map containing discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data includes the following steps: Step 1: Obtain the officially released multidimensional geographic data of the target area from the local government service data management bureau.

[0069] The server obtains official multidimensional geographic data from the Government Service Data Management Bureau. The multidimensional geographic data includes: public service facility data, pedestrian road network topology data, land use classification data, and population statistics spatial data.

[0070] Public service facility data includes the location, quantity, land area, service capacity, and operational status of various facilities. Pedestrian road network topology data includes road centerlines and geometric elements of pedestrian paths, with each element carrying node topological relationships and travel time attributes. This data transforms discrete pedestrian spaces into quantifiable and analyzable structured networks, providing standardized data support for various spatial analyses. Bicycle and vehicular road network topology data can also be selected depending on the target area. Land use classification data includes existing land parcel vector data and planned land parcel vector data. The vector data is based on domestic standards: GB 50137-2011 Classification of Urban Construction Land and GB / T 21010-2017 Classification of Current Land Use. The land use classification data includes the following parts: parcel surface elements (each parcel is a closed surface with precise coordinate boundaries); land use classification codes and names to identify land use attributes, such as where schools / hospitals / commercial areas are located, and where industrial / green spaces are located. Spatial demographic data matches demographic indicators with spatial geographic units, including basic data on spatial boundaries, population size, population structure, population distribution, and population projection. Spatial boundaries are the fundamental carriers of spatial demographic data, providing standardized spatial support for population information, accompanied by unique spatial codes and precise coordinate systems, enabling population data to accurately correspond to specific spatial ranges. Population size quantifies the spatial population scale, mainly covering the total number of permanent residents, registered residents, floating population, and temporary residents. Population structure provides demand guidance, focusing on age structure, gender structure, and special population structure, with different population structures corresponding to different public service needs. Population distribution provides spatial differences, reflecting the agglomeration characteristics of population in different spatial units, mainly including population density and raster data of population spatial distribution, which can intuitively present densely populated areas and underpopulated areas. Population projection provides planning foresight, mainly including short-term and long-term planned population size and population structure projections, population growth rate, mechanical growth and natural growth calculations, as well as zoning population allocation indicators and residential land carrying capacity population estimates, which can predict future spatial distribution and demand changes of the population.

[0071] Step two: Analyze the multidimensional geographic data and convert all the multidimensional geographic data to the selected spatial reference coordinate system.

[0072] The server performs comprehensive analysis of the multidimensional geographic data, clarifying the geometric information, attribute fields, and data formats of various data types, and outlining the data association logic. A suitable coordinate system is selected as the unified geographic coordinate system; the chosen spatial reference coordinate system can be one of WGS1984, CGCS2000, BJ54, Xi'an 80, GCJ-02, BD-09, UTM, or Web Mercator. All analyzed multidimensional geographic data is then uniformly converted to the selected spatial reference coordinate system to eliminate spatial misalignment caused by coordinate system differences and ensure the accuracy of spatial associations among various data types.

[0073] Step 3: Identify and extract discretely distributed facility locations from the multidimensional geographic data, and generate a road network map of the routes from each residential community to each facility location based on the road network.

[0074] Based on the public service facility data after coordinate system one, the server accurately identifies and filters discretely distributed facility locations, eliminating dependent or unlocatable locations, and extracting the geometric coordinates and core attribute information of each location within the selected spatial reference coordinate system, completing the classification, sorting, and standardized archiving. Using the pedestrian road network topology data after coordinate system one as a basis, combined with the residential community spatial area map and the extracted discrete facility locations, a road network map is generated showing the paths from each residential community to the corresponding facility location within the selected spatial reference coordinate system, clarifying the road network topology and traffic-related attributes.

[0075] The above methods integrate multi-dimensional geographic data, covering supply and demand as well as spatial elements, supporting comprehensive analysis. A unified spatial coordinate system ensures accurate and reliable spatial calculations. Automatic extraction of facility locations enables refined resource positioning. Path generation based on real road networks aligns with actual travel patterns, resulting in practical outcomes. This provides technical support for the optimization and scientific planning of public service facility layouts.

[0076] In one embodiment, based on the road network map and facility locations in the target area, residential communities accessible within a predetermined time range of the facilities are determined, and isochronous service zones are generated accordingly. Based on the isochronous service zones, the service coverage rate of the facility locations in the target area is obtained, including the following steps: Step 1: Construct a topological network graph containing nodes and edges based on the road network graph in the target area. Use the travel time of road segments as the edge weights. Based on the topological network graph and the weights of each edge, establish a time cost resistance surface to quantify the travel time of different road segments.

[0077] A topological network graph abstracts geographic space into a network composed of "nodes" and "edges." Nodes represent specific facilities such as hospitals, supermarkets, and parks, while demand points include residential communities or key intersections. Edges represent connecting roads between nodes, and each edge has a weight representing distance, travel time, etc. The server calculates travel time, using the travel time of a road segment as the edge weight for each road segment in the network. Its passage time Calculate according to the following formula:

[0078] in: This is the geometric length (meters) of the road segment. The design speed (meters per second) for this road section is preset with different values ​​based on the road class (such as expressway, secondary road, alley). Road condition correction factor (0 < ≤1), used to reflect the impact of slope, road conditions, or real-time congestion on speed reduction (if based solely on static data, it can be set to ≤1). =1).

[0079] Length of a certain road section =500m, design speed =10m / s, Congestion Correction Factor =0.8, = =62.5 seconds.

[0080] Step 2: Using the facility point as the source point, execute the graph traversal algorithm to calculate the cumulative minimum time cost from the source point to all nodes in the network, generate the shortest path tree, and extract the set of boundary points of the region where the time cost is not greater than the time threshold.

[0081] The server uses the target facility point as the source point and performs a graph traversal of the road network using either Dijkstra's algorithm or A* algorithm. It calculates the cumulative minimum time cost from the source point to all nodes in the network and constructs a shortest path tree. Each node in the tree records the minimum cumulative time to reach that node from the source point and information about its predecessor nodes and paths.

[0082] Threshold truncation and boundary extraction, setting service time thresholds (e.g., 15 minutes), filter from the shortest path tree to find paths that satisfy the minimum cumulative time ≤ All nodes and connected edges constitute a reachable subgraph. Linear interpolation is performed on edges crossing a threshold to accurately locate isochronous boundary points. If an edge exists during the shortest path tree traversal process of the server... An edge is considered a critical edge if it meets the following conditions. Connecting nodes and node v, node Reachable ,node Unreachable And it satisfies: If the boundary condition is met, then the edge is a critical edge. For each critical edge, the system accurately locates the isochronous circle boundary point using linear interpolation. Positional proportions :

[0083] Let the spatial coordinates of nodes u and v be respectively =( , ), =( , Then the boundary point The coordinates are:

[0084] in: From source to node The cumulative minimum time; The set service time threshold (e.g., 15 minutes); For the edge Passage time; , They are nodes and Spatial coordinates.

[0085] By connecting all the interpolated boundary points in spatial topological order, the server can form a closed isochronous circle, which visually represents the location of the facility points. The service coverage area within a given time period is called the isochronous service area.

[0086] Step 3: Calculate the service coverage rate of the facility in the target area, which includes spatial coverage rate and population coverage rate.

[0087] Spatial coverage rate refers to the proportion of the area served by a facility to the total area of ​​the target area. It is used to quantify the service coverage capability of a facility in a spatial dimension. The calculation steps for the server are as follows: Using geographic information processing technology, the total area A of the target area and the area A1 of the service coverage area S1 are calculated respectively. Calculate the spatial coverage R1 using the following formula: R1 *100% The value of R1 ranges from [0, 100%]. The closer R1 is to 100%, the wider the spatial service coverage of the facility and the stronger its spatial service capability. If R1 = 0, it means that the service coverage area of ​​the facility does not involve the target area. If R1 = 100%, it means that all areas within the target area are within the service coverage area of ​​the facility.

[0088] Population coverage rate refers to the proportion of the population within the service area of ​​a facility to the total population of the target area. It is used to quantify the service coverage effectiveness of a facility in terms of population. The calculation steps are as follows: To obtain the total population P of the target area, raster population data, zonal population statistics, etc. can be used to ensure the timeliness and accuracy of the data; By using spatial overlay analysis, the vector boundary of the service coverage area S1 is spatially matched with the population distribution data, all population units within the range of S1 are extracted, and the service coverage population P1 is obtained by summarizing them. Calculate the population coverage rate R² using the following formula: R² *100% The value of R2 ranges from [0, 100%]. The closer R2 is to 100%, the more people the facility can cover and the higher the population service efficiency. If R2 = 0, it means that the facility does not cover any population in the target area. If R2 = 100%, it means that all population in the target area is within the service coverage of the facility.

[0089] The above methods are aligned with actual travel and road network rules, resulting in more realistic and reliable results. They provide more accurate calculations of accessibility and service range, avoid errors, reflect differences in traffic conditions, and provide assessments that are more in line with urban operations. They can identify service blind spots and road network bottlenecks, supporting refined planning.

[0090] In one embodiment, isochronous service areas are overlaid with residential community maps in the target area to determine residential areas within the target area that are not covered by services, thus constructing a dataset of service blind spots, including the following steps: Step 1: Overlay the service area map of the isochronous circle with the residential community map of the target area, and calculate the geometric intersection of each residential community and the service area in the residential community map.

[0091] The server overlays the isochronous service area map with the residential community map in the target area, and calculates the geometric intersection of each residential community in the residential community map with the service area. The service area of ​​the isotime circle and the vector map of the residential community in the target area are overlaid and analyzed. The server calculates the geometric intersection area of ​​each residential community and the service area of ​​the isotime circle one by one, and further counts the number of covered communities, the covered population and the proportion of service area.

[0092] Step 2: If the proportion of a residential community's area falling within the service area is less than a set value, it is identified as a non-compliant community, and all non-compliant communities are extracted as a service blind spot dataset.

[0093] Residential communities with an area ratio less than a set value are identified as substandard communities. The server extracts all substandard communities to construct a service blind spot dataset. A community is considered a service blind spot if its area ratio within the service area is less than 30%, or if it is completely outside the service area. All substandard communities are extracted to form the service blind spot dataset. .

[0094] The above methods offer precise evaluation scales, objective and reliable results, unified judgment standards, strong repeatability, accurate blind spot identification, easy data application, support for optimized infrastructure layout, and strong practicality.

[0095] In one embodiment, the target area is spatially discretized into a grid to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, the initial site selection of the facility within a predetermined time range is determined. The service coverage of the initial site selection of the facility is weighted using the population weight of the target area, and the site selection efficiency index of the initial site selection of the facility is calculated, including the following steps: Step 1: Spatial discretization and gridding are performed on the target area to generate an evaluation grid set G covering the entire target area.

[0096] Set the grid size, typically 10m x 10m at a street scale, but this can be adjusted based on estimated site size. Based on the target area, server, and set grid size, generate an evaluation grid set G={ covering the entire study area. , ,..., Each grid cell is considered a potential candidate site. This enables a full-space, no-blind-spot traversal search of the study area, ensuring that no hidden but highly efficient optimal site is overlooked, thus guaranteeing the global optimality of the solution.

[0097] Step 2: Based on the service blind spot and the evaluation grid set, calculate the coverage and accessibility of each evaluation grid to the service blind spot within a predetermined time range, and select candidate grids that can effectively cover the service blind spot within the predetermined time range as the initial site selection for the facility.

[0098] Starting from communities in service blind spots, the server uses Dijkstra's algorithm or A* algorithm to perform graph traversal of the road network, with a set time threshold. The isochronous circle generates a closed blind zone isochronous circle. This blind zone isochronous circle is expressed starting from the service blind zone community. The area that can be reached on foot within a given time.

[0099] Overlay the evaluation grid and the isochronous loop of the blind zone to determine if there is a spatial topological intersection between them. If they intersect, it indicates that there is a spatial topological intersection within the grid. Facilities located in areas where services can cover blind spots in the community. Evaluation grid at intersections. This was identified as a candidate grid that could effectively serve the blind spots. As the initial site selection for facilities to be constructed, it provides a basic plan for subsequent site selection optimization and final selection of the best location.

[0100] For each candidate grid Define its effective coverage blind spot community set :

[0101] Step 3: Based on the population weight of the target area, perform community population-weighted calculation on the service coverage of the initial site selection of the facility to obtain the corresponding site selection efficiency index.

[0102] The server retrieves each candidate grid. The target service population, in candidate grids Centered on a point, define an isochronous circle, extract all population grid data within that circle, and sum the raster values ​​of all population cells within that circle. The target service population is determined. The calculation of the service population varies depending on the type of facility. For example, for health service facilities, the number of permanent residents in the community is used, while for elderly care facilities, the number of residents aged 60 and above in the community is used. The site selection efficiency index for this grid is then obtained, denoted as... :

[0103] "How many blind spots in community coverage can be addressed at once, and how many people will benefit from the service?" The higher the value, the higher the site selection efficiency, the more people in the blind spots it can serve, and the higher its priority.

[0104] In one embodiment, the population is allocated proportionally to the initial site selection of each facility based on attractiveness and distance decay.

[0105] Calculate all candidate meshes, calculate This value generates a continuous heatmap of location efficiency distribution. It includes the values ​​for each candidate grid cell across the entire region. The raster dataset of values ​​is directly used to guide the subsequent screening and sorting of potential land parcels. For Grid areas with higher values ​​represent overlapping and intersecting service areas for multiple blind spots. Site selection in these locations maximizes the benefit of "building one point to solve an entire blind spot."

[0106] The above methods generate an evaluation grid set through spatial discretization, achieving a precise characterization of regional spatial features and laying an accurate foundation for site selection evaluation. By combining service blind spots with the evaluation grid to determine initial site selection, service gaps are accurately identified, focusing on areas with urgent demand, avoiding resource waste, and improving the accuracy and practicality of initial site selection. A site selection efficiency index is calculated based on population weights, linking precise population data with facility service benefits, quantitatively reflecting the effectiveness of site selection, and promoting the upgrade of evaluation from "spatial accessibility" to "service effectiveness," thereby improving the accuracy and fairness of facility layout.

[0107] In one embodiment, the provided theoretically plannable land parcels and the initial site selection of the facility are screened based on the site selection efficiency index to generate a better combination of site selection schemes, including the following steps: Step 1: Perform spatial overlay analysis on the provided theoretically plannable land parcels and the initial site selection parcels for facilities to accurately identify the overlapping areas between the initial site selection grid and the plannable land parcels.

[0108] The server overlays and analyzes two types of spatial data: theoretically plannable land parcels and initial facility site selection parcels, using geometric intersection analysis. It identifies land use constraints grid-by-grid, accurately extracting the spatial overlap between the initial site selection grid and the plannable land parcels. Site selection points that do not meet construction conditions or conflict with planning are excluded, while valid candidate sites that simultaneously meet the initial site selection requirements and construction conditions are retained. These retained grids are considered potential grids. Potential grid The site has available space for construction, laying a spatial foundation for subsequent site selection optimization. Potential grids will be utilized. By site selection efficiency index Sort in descending order to obtain an ordered candidate list. .

[0109] In one embodiment, the method for extracting theoretically planarable land parcels can also be: The server uses existing land parcel data as a baseline layer and planned land parcel data as an overlay layer. Based on the boundary geometry information of the overlay layer, it performs topological cutting on the baseline layer to generate cut patches. These cut patches reflect the spatial distribution differences between the two layers. A spatial location index is established, and the planned land use type attributes of the overlay layer are mapped to the cut patches through spatial connections. The cut patches also inherit the existing land use type attributes of the existing land use patches. This ensures that each patch in the newly generated vector dataset contains both existing and planned land use type attributes. The difference between the existing and planned land use types of the cut patches is assessed, and patches with inconsistent major land use categories are extracted as potential development sites—theoretically plannable sites. For example: [Image of a patch]. The current land use type is U12 power supply, and the major category is U; the planned land use type is Rr1 Class I residential land, and the major category is R. Based on the logical judgment, the land use type and major category are inconsistent, so this plot is extracted as a plot with construction potential.

[0110] Step 2: Based on the retained potential grid By comprehensively considering constraints such as coverage balance, accessibility, and construction costs, the facility locations are combined and optimized to form a set of superior facility site selection schemes.

[0111] With potential grid To establish the basic spatial unit for site selection, the server constructs a multi-objective optimization model aimed at maximizing coverage balance, maximizing spatial accessibility, and minimizing total construction cost. Under conditions of spatial constraints, service living circles, and cost constraints, it performs global optimization of facility location combinations, outputting multiple sets of superior facility location schemes to support the final site selection decision. Among these candidate facility location combinations, the model is selected based on a comprehensive evaluation of key indicators such as coverage balance, accessibility, and construction cost, identifying the one with the best overall benefits and determining it as the final recommended facility layout scheme.

[0112] in: Coverage balance, each potential grid The spatial uniformity of service load and coverage is quantified using the standard deviation of service volume and the coefficient of variation to avoid situations where some facilities are overloaded while others are idle. Spatial accessibility and potential grid are also considered. The accessibility and time convenience to service communities are quantified using road network distance or time cost calculations, making the distance to nearby facilities shorter and reducing travel time for each residential community. Construction costs, including construction fees and subsequent operation and maintenance costs, are controlled to manage the overall investment costs in equipment procurement, site deployment, and subsequent operation and maintenance while meeting service needs.

[0113] The above three requirements are integrated into a multi-objective optimization model. Combined with conventional constraints such as service coverage, number of servers deployed, and service distance, the optimal server location and deployment scheme is selected through iterative calculation using intelligent optimization algorithms, which takes into account convenient access, balanced distribution, and cost savings.

[0114] In one embodiment, the server obtains input location requirements, such as the need to build two types of facilities, and the server selects from a potential grid set. In this process, all grids are combined, and each combination is a candidate solution, forming a solution set.

[0115]

[0116] For the solution set Each element in Medium grid The corresponding effective coverage blind spot community set Perform a union operation to obtain the union. :

[0117] That is, combination scheme Given a set of communities in the effective coverage blind spots, sum the population contribution value of each community in the blind spots. To obtain the site selection efficiency index of the scheme. .

[0118] Iterate through the set of schemes K to calculate the location efficiency index E for each scheme, and output the set of schemes K in descending order of location efficiency index E.

[0119] The server outputs the final optimal solution, selecting the one from the solution set K that offers the best overall benefits, maximizing coverage balance, maximizing spatial accessibility, and minimizing total construction cost. This is then chosen as the final recommended facility layout solution.

[0120] The above method can accurately extract locations with site selection value, significantly reducing the time and manpower costs of site selection analysis and improving site selection efficiency. Using the site selection efficiency index as the core ranking criterion, it can intuitively highlight high-quality candidate locations with higher site selection efficiency and stronger comprehensive adaptability, providing clear priority guidance for subsequent scheme combinations and reducing blind screening. Combining the facility quantity requirements set by the user to generate a better combination of site selection schemes not only takes into account the user's actual construction needs, but also relies on the accurate screening and scientific ranking in the early stage to ensure that the generated scheme combination achieves optimal adaptability in terms of coverage balance, accessibility and construction cost.

[0121] In one embodiment, a living circle planning evaluation device based on spatial supply and demand matching is provided. The device includes a data acquisition module, a facility service efficiency evaluation module, a service blind spot identification module, a site selection efficiency quantification module, and a site selection scheme generation module.

[0122] The data acquisition module is used to acquire multidimensional geographic data of the target area and extract discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data.

[0123] The facility service efficiency assessment module is used to determine the residential communities accessible within a predetermined time range of the facilities based on the road network map and the facility locations in the target area, generate isochronous service areas accordingly, and calculate the service coverage rate of the facility locations in the target area based on the isochronous service areas using network analysis.

[0124] The service blind spot identification module is used to spatially overlay the isochronous service area with the existing map of the target area to locate the uncovered areas within the target area and construct a service blind spot dataset.

[0125] The site selection efficiency quantification module performs spatial discrete grid processing on the target area to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, it determines the initial site selection of facilities that can be reached within a predetermined time range. Based on the population weight of the target area and combined with the service coverage of the initial site selection of facilities, it performs a weighted summation to calculate the site selection efficiency index of the initial site selection of facilities.

[0126] The site selection scheme generation module identifies potential plots from the provided existing and planned plots, and generates a better combination of site selection schemes based on the potential plots and the site selection efficiency index.

[0127] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring multidimensional geographic data of a target area; extracting from the multidimensional geographic data a road network map containing discretely distributed facility locations and each residential community to each facility location; determining, based on the road network map and the facility locations in the target area, the residential communities accessible within a predetermined time range of the facilities, and generating isotime service zones accordingly; and obtaining the service coverage rate of the facility locations in the target area based on the isotime service zones; and applying the isotime service zones... By overlaying a layer onto a residential community map within the target area, unserved residential areas within the target area are identified, constructing a dataset of service blind spots. The target area is then spatially discretized into a grid to generate an evaluation grid set. Based on the service blind spots and the evaluation grid set, initial facility locations reachable within a predetermined timeframe are determined. A weighted sum is calculated based on the population weight of the target area and the service coverage of the initial facility locations, yielding a location efficiency index. The provided theoretically planarable land parcels and the initial facility locations are then filtered and optimized using the location efficiency index to generate a superior combination of location schemes.

[0128] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating living circle planning based on spatial supply and demand matching, characterized in that, include: Acquire multidimensional geographic data of the target area, and extract discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data; Based on the road network map and facility locations in the target area, determine the residential communities accessible within a predetermined time range of the facilities, and generate isochronous service areas accordingly. Based on the isochronous service areas, obtain the service coverage rate of the facility locations in the target area. By overlaying the isochronous service area with the residential community map in the target area, the residential areas in the target area that are not covered by services are identified, and a dataset of service blind spots is constructed. The target area is spatially discretized into a grid to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, the initial site selection of the facility that can be reached within a predetermined time range is determined. The service coverage of the initial site selection of the facility is weighted by the population weight of the target area, and the site selection efficiency index of the initial site selection of the facility is calculated. Based on the site selection efficiency index, the provided theoretically plannable land parcels and the initial site selection of the facilities are screened to generate a better combination of site selection schemes.

2. The living circle planning evaluation method based on spatial supply and demand matching according to claim 1, characterized in that, The acquisition of multidimensional geographic data of the target area, and the extraction of discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data, include: Define the target area and acquire multidimensional geographic data within the target area, including public service facility data, pedestrian road network topology data, land use classification data, and population spatial data; The multidimensional geographic data is parsed, and all multidimensional geographic data are uniformly transformed to the selected spatial reference coordinate system; Discretely distributed facility locations are identified and extracted from the multidimensional geographic data, and a road network map of the routes from each residential community to each facility location is generated based on the road network.

3. The living circle planning evaluation method based on spatial supply and demand matching according to claim 1, characterized in that, The step of determining the residential communities reachable by the facilities within a predetermined time range based on the road network map and facility locations in the target area, and generating isotime service zones accordingly, and obtaining the service coverage rate of the facility locations in the target area based on the isotime service zones, includes: Based on the road network map in the target area, a topological network graph containing nodes and edges is constructed. The travel time of road segments is used as the edge weight. Based on the topological network graph and the weights of each edge, a time cost resistance surface is established to quantify the travel time of different road segments. Using the facility point as the source point, execute the graph traversal algorithm to calculate the cumulative minimum time cost from the source point to all nodes in the network, generate the shortest path tree, and extract the set of boundary points of the region where the time cost is not greater than the time threshold. Based on the boundary point set, a closed isochronous service area is generated using a geometric closure algorithm; Calculate the service coverage of the facility in the target area, whereby the service coverage includes spatial coverage and population coverage.

4. The living circle planning evaluation method based on spatial supply and demand matching according to claim 3, characterized in that, The method for calculating the isochronous cycle is as follows: Set time threshold Filter out cumulative time costs ≤ The reachable subgraph; linear interpolation is performed on edges that cross the threshold to accurately locate isochronous boundary points; During the shortest path tree traversal, if there exists an edge Connect to reachable nodes and unreachable nodes And satisfy: Then this edge is a critical edge. The system calculates the isochronous boundary points on this edge using linear interpolation. Positional proportions : Boundary points The coordinates (x, y) are calculated as follows: in From source to node The cumulative minimum time, For the set service time threshold, For the edge The passage time, , They are nodes and Spatial coordinates.

5. The living circle planning evaluation method based on spatial supply and demand matching according to claim 1, characterized in that, The process involves overlaying the isochronous service area with the residential community map in the target area to determine the residential areas within the target area that are not covered by services, thus constructing a dataset of service blind spots, including: Overlay the service area map of the isochronous circle with the residential community map of the target area, and calculate the geometric intersection of each residential community and the service area in the residential community map. If the proportion of a residential community falling within the service area is less than a set value, it is identified as a substandard community, and all substandard communities are extracted as a service blind spot dataset.

6. The living circle planning evaluation method based on spatial supply and demand matching according to claim 1, characterized in that, The process involves spatially discretizing and gridding the target area to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, initial facility locations reachable within a predetermined time frame are determined. The service coverage of the initial facility locations is weighted using the population weight of the target area, and a location efficiency index for the initial facility locations is calculated, including: The target region is spatially discretized into a grid to generate an evaluation grid set G covering the entire target region; Based on the service blind zone and the evaluation grid set, the coverage and accessibility of each evaluation grid to the service blind zone within a predetermined time range are calculated, and candidate grids that can effectively cover the service blind zone within the predetermined time range are selected as the initial site selection for the facility. By using the population weight of the target area, the service coverage area of ​​the initial site selection of the facility is calculated by weighting the community population, and the corresponding site selection efficiency index is obtained.

7. The living circle planning evaluation method based on spatial supply and demand matching according to claim 1, characterized in that, The identification of theoretically planarable land parcels includes: Spatial overlay analysis was used to analyze existing and planned land parcel data to identify potential land parcels with the conditions for new facility construction; Spatial intersection analysis is performed on the initial facility sites and the potential land parcels to extract all initial facility sites that spatially intersect with the potential land parcels, thus constructing a potential site set. According to the site selection efficiency index, for All grids are sorted in descending order to obtain an ordered candidate list. ; Based on the user's specified facility quantity requirements, generate a combination of optimal site selection options.

8. The living circle planning evaluation method based on spatial supply and demand matching according to claim 7, characterized in that, The spatial overlay analysis of existing and planned land parcel data identifies potential land parcels with conditions for new facility construction, including: Using existing land parcel data as the baseline layer and planned land parcel data as the overlay layer, the baseline layer is topologically cut based on the boundary geometric information of the overlay layer to generate cut patches; Establish a spatial location index and map the planned land use type attributes of the overlay layer to the cut plots through spatial connections; The differences between the current land use type and the planned land use type of the cut map patches are judged, and the map patches with inconsistent major land use types are extracted as potential construction plots.

9. A living circle planning evaluation device based on spatial supply and demand matching, characterized in that, The device includes: The data acquisition module is used to acquire multidimensional geographic data of the target area and extract discretely distributed facility locations and road network maps from each residential community to each facility location from the multidimensional geographic data. The facility service efficiency assessment module is used to determine the residential communities reachable within a predetermined time range of the facility based on the road network map and the facility locations in the target area, and generate isochronous service areas accordingly. Using network analysis, the module calculates the service coverage rate of the facility locations in the target area based on the isochronous service areas. The service blind spot identification module is used to spatially overlay the isochronous service area with the existing map of the target area to locate the uncovered area within the target area and construct a service blind spot dataset. The site selection efficiency quantification module performs spatial discrete grid processing on the target area to generate an evaluation grid set. Based on the service blind spot and the evaluation grid set, it determines the initial site selection of facilities that can be reached within a predetermined time range. It uses the population weight of the target area to weight the service coverage of the initial site selection of facilities and calculates the site selection efficiency index of the initial site selection of facilities. The site selection scheme generation module identifies potential plots from the provided existing and planned plots, and generates a better combination of site selection schemes based on the potential plots and the site selection efficiency index.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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